Broadcast and multicast transmission in a distributed massive mimo network

ABSTRACT

Systems and methods for broadcast/multicast transmission in a distributed cell-free massive Multiple Input Multiple Output (MIMO) network are disclosed. In one embodiment, a method for broadcasting/multicasting data to User Equipments (UEs) comprises, at each Access Point (AP) of two or more APs, obtaining long-term Channel State Information (CSI) for a UE(s) and communicating the long-term CSI for the UE to a central processing system. The method further comprises, at the central processing system, receiving the long-term CSI for the UE from each AP, computing a precoding vector (w) for the UE(s) across the APs based on the long-term CSI, and communicating the precoding vector (w) to the APs. The method further comprises, at each AP, obtaining the precoding vector (w) from the central processing system, precoding data to be broadcast/multicast to the UE(s) based on the precoding vector (w), and broadcasting/multicasting the precoded data to the UE(s).

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/851,951, filed May 23, 2019, the disclosure of whichis hereby incorporated herein by reference in its entirety.

TECHNICAL FIELD

The present disclosure relates to a Distributed Massive Multiple InputMultiple Output (DM-MIMO) system and, in particular, to broadcast ormulticast transmission in a DM-MIMO system.

BACKGROUND

Massive Multiple Input Multiple Output (MIMO), which is also known aslarge-scale antenna systems and very large MIMO, is a multi-user MIMOtechnology where each base station is equipped with a large number ofantenna elements (typically more than 50), which are being used to servemany terminals that share the same time and frequency band and areseparated in the spatial domain. A key assumption is that there are manymore base station antennas than terminals—at least twice as many, butideally as many as possible. Massive MIMO offers many benefits overconventional multi-user MIMO. First, conventional multi-user MIMO is nota scalable technology since it has been designed to support systems withroughly equal numbers of service antennas and terminals, and practicalimplementations of conventional multi-user MIMO typically relies onFrequency Division Duplexing (FDD) operation. By contrast, in massiveMIMO, the large excess of service antennas over active terminalsoperating in Time Division Duplexing (TDD) brings large improvements inthroughput and radiated energy efficiency. These benefits result fromthe strong spatial multiplexing achieved by appropriately shaping thesignals sent out and received by the base station antennas. By applyingprecoding to all antennas, the base station can ensure constructiveinterference among signals at the locations of the intended terminals,and destructive interference almost everywhere else. Furthermore, as thenumber of antennas increases, the energy can be focused with extremeprecision into small regions in space. Other benefits of massive MIMOinclude use of simple low-power components since it relies on simplesignal processing techniques, reduced latency, and robustness againstintentional jamming.

When operating in TDD mode, massive MIMO can exploit the channelreciprocity property, according to which the channel responses are thesame in both uplink and downlink. Channel reciprocity allows the basestations to acquire Channel State Information (CSI) from pilot sequencestransmitted by the terminals in the uplink, and this CSI is then usefulfor both the uplink and the downlink. By the law of large numbers, theeffective scalar channel gain seen by each terminal is close to adeterministic constant. This is called channel hardening. Thanks to thechannel hardening, the terminals can reliably decode the downlink datausing only long-term statistical CSI, making the most of the physicallayer control signaling redundant, i.e. low-cost CSI acquisition. Thisrenders the conventional resource allocation concepts unnecessary andresults in a simplification of the Medium Access Control (MAC) layer.These benefits explain why massive MIMO has a central position in FifthGeneration (5G).

However, massive MIMO system performances are affected by some limitingfactors. Channel reciprocity requires hardware calibration. In addition,the so-called pilot contamination effect is a basic phenomenon whichprofoundly limits the performance of massive MIMO systems.Theoretically, every terminal in a massive MIMO system could be assignedan orthogonal uplink pilot sequence. However, the maximum number oforthogonal pilot sequences that can exist is upper-bounded by the sizeof the coherence interval, which is the product of the coherence timeand coherence bandwidth. Hence, adopting orthogonal pilots leads toinefficient resource allocation as the number of the terminalsincreases, or it is not physically possible to perform when thecoherence interval is too short. As a consequence, pilots must be reusedacross cells, or even within the home cell (for higher cell density).This inevitably causes interference among terminals which share the samepilot. Pilot contamination does not vanish as the number of base stationantennas grows large, and so it is the one impairment that remainsasymptotically.

To implement massive MIMO in wireless networks, two differentarchitectures can be adopted:

-   -   Centralized Massive MIMO (C-maMIMO), where all the antennas are        co-located in a compact area at both the base station and user        sides, as shown in FIG. 1. It represents the conventional        massive MIMO system.    -   Distributed Massive MIMO (D-maMIMO), where base station        antennas, herein named Access Points (APs), are geographically        spread out over a large area in a well-planned or random        fashion, as shown in FIG. 2. Antennas are connected together and        to a Central Processing Unit (CPU) through high-capacity        backhaul links (e.g., fiber optic cables). It is also known as a        cell-free massive MIMO system.

D-maMIMO architecture is one important enabler of network MIMO in futurestandards. Network MIMO is a terminology that is used for a cell-freewireless network, where all the base stations that are deployed over thecoverage area act as a single base station with distributed antennas.This can be considered the ideal network infrastructure from aperformance perspective, since the network has great abilities tospatially multiplex users and exactly control the interference that iscaused to everyone.

The distinction between D-maMIMO and conventional distributed MIMO isthe number of antennas involved in coherently serving a given user. InD-maMIMO, every antenna serves every user. Compared to C-maMIMO,D-maMIMO has the potential to improve both the network coverage and theenergy efficiency due to increased macro-diversity gain. This comes atthe price of higher fronthaul requirements and the need for distributedsignal processing. In D-maMIMO, the information regarding payload dataand power control coefficients is exchanged via the backhaul networkbetween the APs and the CPU. There is no exchange of instantaneous CSIamong the APs or the central unit, i.e., CSI acquisition can beperformed locally at each AP.

Due to network topology, D-maMIMO suffers from different degrees of pathlosses caused by different access distances to different distributedantennas, and very different shadowing phenomena that are notnecessarily better (e.g., antennas deployed at the street level are moreeasily blocked by buildings than antennas deployed at elevatedlocations). Moreover, since the location of antennas in D-maMIMO has asignificant effect on the system performance, optimization of theantenna locations is crucial. In addition, the D-maMIMO systempotentially suffers a low degree of channel hardening. As mentionedearlier, the channel hardening property is key in massive MIMO tosuppress small-scale fading and derives from the large number ofantennas involved in a coherent transmission. In D-maMIMO, APs aredistributed over a wide area, and many APs are very far from a givenuser. Therefore, each user is effectively served by a smaller number ofAPs. As a result, channel hardening might be less pronounced. This wouldconsiderably affect the system performance.

The performance of any wireless network is clearly the availability ofgood enough CSI to facilitate phase-coherent processing at multipleantennas. Intuitively, acquiring high quality CSI should be easier witha C-maMIMO than in a D-maMIMO where the antennas are distributed over alarge geographical area. Nevertheless, the macro-diversity gain has adominant importance and leads to improved coverage and energyefficiency.

A problem with a massive MIMO deployment is that a large number ofantennas generate a large amount of data. This implies that, withtraditional radio to antenna interfaces, very large capacity fibernetworks are needed to shuffle this data around. Fiber is both expensiveand needs skilled personnel for installation, both of which limit thedeployment scenarios for massive MIMO. There is also a scalability issueas different size baseband units are needed to handle different arraysizes, e.g. one to handle 32 antennas, another one for 128 antennas,etc.

From a practical point of view, a C-maMIMO solution where all antennaelements (i.e., APs) are placed close together has a number of drawbackscompared to a D-maMIMO solution where the antenna elements aredistributed over a larger area. These are, e.g.:

-   -   Very large service variations: User Equipment devices (UEs) that        happen to be located close to the central massive MIMO node will        experience very good service quality while for UEs further away        the service quality will degrade rapidly.    -   Sensitive to blocking: On high frequency bands in particular the        signal is easily blocked by obstacles that obscure the        line-of-sight between the UE and the C-maMIMO node. In D-maMIMO,        a number of antenna elements may be blocked but it requires much        larger obstacles to block all antenna elements    -   High heat concentration: Due to heat concentration it is        difficult to make C-maMIMO nodes very small. In D-maMIMO, each        antenna element (and its associated processing) generates only a        small amount of heat and this simplifies miniaturization.    -   Large and visible installations: C-maMIMO installations can        become large, especially on lower frequency bands. D-maMIMO        installations are actually even larger, but the visual impact        can be made almost negligible.    -   Installation requires personnel with “radio skills”: Installing        a complex piece of hardware in a single location requires        planning and most probably also proper installation by certified        personnel. In a D-maMIMO installation it is less crucial that        each and every one of the very many antenna elements is        installed in a very good location. It is sufficient that the        majority of the elements are installed in good enough locations.        The requirements on installation can be significantly relaxed        with a D-maMIMO deployment.    -   Power limited by regulations (e.g., Specific Absorption Rate        (SAR)): If the antenna elements are located close together there        will be an area close to the installation where electromagnetic        wave safety rules apply. This is likely to put limits on the        total radiated radio frequency power in many installations. In a        D-maMIMO installation a user may come close to a small number of        antenna elements, but it is impossible to be physically close to        many elements that are distributed over a large area.

There are many significant benefits with D-maMIMO compared to C-maMIMO.However, the cabling and internal communication between antenna elementsin a D-maMIMO is prohibiting in state-of-the art solutions. It is noteconomically feasible to connect a separate cable between each antennaelement and a CPU (e.g., in a star topology) in a D-maMIMO installation.Either arbitrary or optimal AP topology may lead to a prohibitive costfor the backhaul component, as well as installation costs fordistributing processing and settings.

The principle of “radio stripes” in was previously introduced in WO2018/103897 A1, entitled IMPROVED ANTENNA ARRANGEMENT FOR DISTRIBUTEDMASSIVE MIMO.

The actual “base stations” in a “radio stripe system” may consist ofcircuit mounted chips inside a protective casing of a cable or a stripe.The receive and transmit processing of each antenna element is performednext to the actual antenna element itself. Since the total number ofdistributed antenna elements is assumed to be large (e.g., severalhundred), the radio frequency transmit power of each antenna element isvery low.

The example in FIG. 3 depicts a system mockup and shows a Light EmittingDiode (LED) lighting stripe connected to a box. This figure is only usedto exemplify how the actual distributed massive MIMO base station couldbe built. A CPU (or stripe station) connects with one or more radiostripes (or distributed MIMO active antenna cables).

The actual radio stripes may contain tape or adhesive glue on thebackside, as in the example of the LED stripes, or it may simply containvery small per-antenna processing units and antennas protected by theplastics covering the cable.

An important observation to make is that both the transmitter andreceiver processing can be distributed under certain assumptions, e.g.see FIG. 4. With low-complexity precoding methods such as conjugatebeamforming, each antenna element can be equipped with a controllingentity (Antenna Processing Unit (APU)) that determines the beamformingweights without communicating with all other APUs.

One area where distributed massive MIMO is of interest is on-demandbroadcast and/or multicast transmissions in 5G New Radio (NR), over acertain region or complete coverage area. Existing Fourth Generation(4G) Long Term Evolution (LTE) systems support broadcast and multicasttransmission over a wide area using either Single Frequency Network(SFN) or single-cell point-to-multipoint operating modes, under thecurrent moniker of Multimedia Broadcast Multicast Service (MBMS).Specifically, in Multimedia Broadcast Multicast Service Single FrequencyNetwork (MBSFN), base stations across multiple cells transmit the samedata in the same resource block over special frames dedicated to MBMSservice. Alternatively, in Single Cell Point to Multipoint (SC-PTM), thesame data is transmitted to multiple users in a single cell usingPhysical Downlink Shared Channel (PDSCH). This is expected to soon besupported also in 5G NR access technology. However, the radio sessionestablishment procedure of LTE MBMS is complex and time-consuming. Inany of the above modes, MBMS requires a separate user planeinfrastructure for connecting the Radio Access Network (RAN) with thecore network. Furthermore, broadcast transmission in LTE is always anadd-on feature with the above limitations. It is not straightforward todirectly adapt LTE MBMS for 5G NR while simultaneously enabling fast andefficient radio sessions as well as joint unicast, broadcast, andmulticast transmissions.

Depending on the application, low-rate and high-rate broadcasttransmission may be needed with a guaranteed reliability. The firstexample includes broadcasting of signals during initial access operationfor synchronization, system information, and paging, which must betransmitted over a wide area, but not necessarily with high-rate. Insome situations, such as live events or in stadiums, a relativelyhigh-rate data transmission may be needed, and there are no time orradio resources to acquire instantaneous or small-scale CSI by means ofuplink reference signals before transmitting data to a UE.

Thus, there is a need for systems and methods for efficient use ofbeamforming, such as the open-loop transmission used in LTE or NR, in anetwork with a distributed massive MIMO deployment.

SUMMARY

Systems and methods for broadcast or multicast transmission in adistributed cell-free massive Multiple Input Multiple Output (MIMO)network are disclosed. In one embodiment, a method for broadcasting ormulticasting data to User Equipments (UEs) in a distributed cell-freemassive MIMO network comprises, at each Access Point (AP) of two or moreAPs, obtaining long-term Channel State Information (CSI) for at leastone UE and communicating the long-term CSI for the at least one UE to acentral processing system. The method further comprises, at the centralprocessing system, for each AP of the two or more APs, receiving thelong-term CSI for the at least one UE from the AP, computing a precodingvector (w) for the at least one UE across the two or more APs based onthe long-term CSI for the at least one UE received from the two or moreAPs, and communicating the precoding vector (w) to the two or more APs.The method further comprises, at each AP of the two or more APs,obtaining the precoding vector (w) from the central processing system,precoding data to be broadcast or multicast to the at least one UE basedon the precoding vector (w) and broadcasting or multicasting theprecoded data to the at least one UE. In this manner, an efficient wayof on-demand data broadcasting in distributed or cell-free massive MIMOnetwork can be provided. Further, the reliability of thebroadcasting/multicasting can be vastly improved. In addition, thisscheme suitable for operating at both micro and millimeter-wavefrequencies.

Embodiments of a method performed at a central processing system arealso provided. In one embodiment, a method performed at a centralprocessing system for a distributed cell-free massive MIMO network forbroadcasting or multicasting data to UEs comprises, for each AP of twoor more APs in the distributed cell-free massive MIMO network, receivinglong-term Channel State Information (CSI) for at least one UE from theAP, computing a precoding vector (w) for the at least one UE across allof the two or more APs based on the long-term CSI for the at least oneUE received from the two or more APs, and communicating the precodingvector (w) to the two or more APs.

In one embodiment, the at least one UE is two or more UEs.

In one embodiment, computing the precoding vector (w) comprises: (a) atiteration 0, initializing the precoding vector (w) to provide aprecoding vector w(0) for iteration 0, (b) computing local-averageSignal to Interference plus Noise Ratios (SINRs) of the at least one UE,(c) identifying a weakest UE from among the at least one UE based on thecomputed local-average SINRs, (d) at iteration n+1, updating theprecoding vector (w) based on the long-term CSI obtained from the atleast one UE for the weakest UE to thereby provide a precoding vectorw(n+1) for iteration n+1, (e) normalizing the precoding vector w(n+1)for iteration n+1, and (f) repeating steps (b) through (e) until astopping criterion is satisfied such that the normalized precodingvector for the last iteration is provided as the precoding vector (w).In one embodiment, the stopping criterion is convergence or maximumnumber of iterations has been reached.

In one embodiment, initializing the precoding vector (w) comprisesinitializing the precoding vector (w) to a value

${w(0)} = {{\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}.}$

In one embodiment, computing the local SINRs of the at least one UEcomprises computing the local SINRs of the at least one UE asγ_(k)=Pw*Θ_(k)w, k=1, . . . , K, by means of Θ_(k). In one embodiment,identifying the weakest UE as the UE whose index k′ is

$k^{\prime} = {\underset{{k = 1},\ldots,K}{\arg\min}{\gamma_{k}.}}$

In one embodiment, updating the precoding vector (w) comprises updatingthe precoding vector (w) at iteration n+1 as w(n+1) =(I+μΘ_(k),)w(n).

In one embodiment, normalizing the precoding vector w(n+1) for iterationn+1 comprises normalizing the precoding vector w(n+1) for iteration n+1as w(n+1)=w(n+1)/∥w(n+1)∥.

In one embodiment, the long-term CSI comprises estimated path loss.

In one embodiment, a coherent interval of the long-term CSI spansgreater than 1,000 symbols, and the method further comprises schedulingtransmissions by the at least one UE (506) of a dedicated pilot.

Corresponding embodiments of a central processing system are alsodisclosed. In one embodiment, a central processing system for adistributed cell-free massive MIMO network for broadcasting ormulticasting data to UEs is adapted to, for each AP of two or more APsin the distributed cell-free massive MIMO network, receive long-term CSIfor at least one UE from the AP, compute a precoding vector (w) for theat least one UE across all of the two or more APs based on the long-termCSI for the at least one UE received from the two or more APs, andcommunicate the precoding vector (w) to the two or more APs.

In one embodiment, a central processing system for a distributedcell-free massive MIMO network for broadcasting or multicasting data toUEs comprises a network interface and processing circuitry associatedwith the network interface. The processing circuitry is configured tocause the central processing system to, for each AP of two or more APsin the distributed cell-free massive MIMO network, receive long-term CSIfor at least one UE from the AP, compute a precoding vector (w) for theat least one UE across all of the two or more APs based on the long-termCSI for the at least one UE received from the two or more APs, andcommunicate the precoding vector (w) to the two or more APs.

Embodiments of a method performed at an AP are also disclosed. In oneembodiment, a method performed at an AP in a distributed cell-freemassive MIMO network for broadcasting or multi-casting data to UEswherein the network comprises two or more APs, comprises obtaininglong-term CSI for at least one UE, communicating the long-term CSI forthe at least one UE to a central processing system, obtaining aprecoding vector (w) from the central processing system where theprecoding vector (w) is for the at least one UE across all of two ormore APs, precoding data to be broadcast or multicast to the at leastone UE based on the precoding vector (w), and broadcasting ormulticasting the precoded data to the at least one UE.

In one embodiment, the at least one UE is two or more UEs.

In one embodiment, the long-term CSI comprises estimated path loss.

In one embodiment, a coherent interval of the long-term CSI spansgreater than 1,000 symbols, and obtaining the long-term CSI for the atleast one UE comprises estimating the long-term CSI for the at least oneUE in fixed intervals of time, based on transmissions of a dedicatedpilot by the at least one UE.

Corresponding embodiments of an AP are also disclosed. In oneembodiment, an AP in a distributed cell-free massive MIMO network forbroadcasting or multi-casting data to UEs wherein the network comprisestwo or more APs is provided, where the AP is adapted to obtain long-termCSI for at least one UE, communicate the long-term CSI for the at leastone UE to a central processing system, obtain a precoding vector (w)from the central processing system where the precoding vector (w) is forthe at least one UE across all of two or more APs, precode data to bebroadcast or multicast to the at least one UE based on the precodingvector (w), and broadcast or multicast the precoded data to the at leastone UE.

In one embodiment, an AP in a distributed cell-free massive MIMO networkfor broadcasting or multi-casting data to UEs wherein the networkcomprises two or more APs is provided, where the AP comprises a networkinterface, at least one transmitter, at least one receiver, andprocessing circuitry associated with the network interface, the at leastone transmitter, and the at least one receiver. The processing circuitryis configured to cause the AP to obtain long-term CSI for at least oneUE, communicate the long-term CSI for the at least one UE to a centralprocessing system, obtain a precoding vector (w) from the centralprocessing system where the precoding vector (w) is for the at least oneUE across all of two or more APs, precode data to be broadcast ormulticast to the at least one UE based on the precoding vector (w), andbroadcast or multicast the precoded data to the at least one UE.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawing figures incorporated in and forming a part ofthis specification illustrate several aspects of the disclosure, andtogether with the description serve to explain the principles of thedisclosure.

FIG. 1 illustrates Centralized massive Multiple Input Multiple Output(C-maMIMO);

FIG. 2 illustrates Distributed massive Multiple Input Multiple Output(D-maMIMO);

FIG. 3 illustrates an example of a stripe architecture for a D-maMIMOsystem;

FIG. 4 illustrates an example in which both the transmitter and receiverprocessing in a D-maMIMO system are distributed;

FIG. 5 illustrates one example of a D-maMIMO system in which embodimentsof the present disclosure may be implemented;

FIG. 6 illustrates a comparison of the cumulative distribution of thelocal-average Signal to Interference plus Noise Ratio (SINR) for abaseline precoder and an iteratively optimized precoder in accordancewith an example embodiment of the present disclosure;

FIG. 7 illustrates the performance advantage of the example embodimentof the iteratively optimized precoder;

FIG. 8 illustrate an iterative process for computing a precoding weightvector for broadcasting/multicasting data to User Equipments (UEs) frommultiple Access Points (APs) in a D-maMIMO system in accordance with oneembodiment of the present disclosure;

FIG. 9 illustrates the operation of the D-maMIMO system of FIG. 5 in thecontext of a Fifth Generation (5G) New Radio (NR) access network inaccordance with some embodiments of the present disclosure;

FIGS. 10 and 11 are schematic block diagrams of example embodiments ofan AP in accordance with the present disclosure;

FIGS. 12 through 14 are schematic block diagrams of example embodimentsof central processing system in accordance with the present disclosure;

FIG. 15 illustrates an example embodiment of a communication system inwhich embodiments of the present disclosure may be implemented;

FIG. 16 illustrates example embodiments of the host computer, basestation, and UE of FIG. 15; and

FIGS. 17 through 20 are flow charts that illustrate example embodimentsof methods implemented in a communication system such as that of FIG.15.

DETAILED DESCRIPTION

The embodiments set forth below represent information to enable thoseskilled in the art to practice the embodiments and illustrate the bestmode of practicing the embodiments. Upon reading the followingdescription in light of the accompanying drawing figures, those skilledin the art will understand the concepts of the disclosure and willrecognize applications of these concepts not particularly addressedherein. It should be understood that these concepts and applicationsfall within the scope of the disclosure.

Generally, all terms used herein are to be interpreted according totheir ordinary meaning in the relevant technical field, unless adifferent meaning is clearly given and/or is implied from the context inwhich it is used. All references to a/an/the element, apparatus,component, means, step, etc. are to be interpreted openly as referringto at least one instance of the element, apparatus, component, means,step, etc., unless explicitly stated otherwise. The steps of any methodsdisclosed herein do not have to be performed in the exact orderdisclosed, unless a step is explicitly described as following orpreceding another step and/or where it is implicit that a step mustfollow or precede another step. Any feature of any of the embodimentsdisclosed herein may be applied to any other embodiment, whereverappropriate. Likewise, any advantage of any of the embodiments may applyto any other embodiments, and vice versa. Other objectives, features,and advantages of the enclosed embodiments will be apparent from thefollowing description.

Radio Node: As used herein, a “radio node” is either a radio access nodeor a wireless device.

Radio Access Node: As used herein, a “radio access node” or “radionetwork node” is any node in a Radio Access Network (RAN) of a cellularcommunications network that operates to wirelessly transmit and/orreceive signals. Some examples of a radio access node include, but arenot limited to, a base station (e.g., a NR base station (gNB) in a ThirdGeneration Partnership Project (3GPP) 5G NR network or an enhanced orevolved Node B (eNB) in a 3GPP LTE network), a high-power or macro basestation, a low-power base station (e.g., a micro base station, a picobase station, a home eNB, or the like), and a relay node.

Core Network Node: As used herein, a “core network node” is any type ofnode in a core network. Some examples of a core network node include,e.g., a Mobility Management Entity (MME), a Packet Data Network Gateway(P-GW), a Service Capability Exposure Function (SCEF), or the like.

Wireless Device: As used herein, a “wireless device” is any type ofdevice that has access to (i.e., is served by) a cellular communicationsnetwork by wirelessly transmitting and/or receiving signals to a radioaccess node(s). Some examples of a wireless device include, but are notlimited to, a UE in a 3GPP network and a Machine Type Communication(MTC) device.

Network Node: As used herein, a “network node” is any node that iseither part of the radio access network or the core network of acellular communications network/system.

Note that the description given herein focuses on a 3GPP cellularcommunications system and, as such, 3GPP terminology or terminologysimilar to 3GPP terminology is oftentimes used. However, the conceptsdisclosed herein are not limited to a 3GPP system.

Note that, in the description herein, reference may be made to the term“cell”; however, particularly with respect to 5G NR concepts, beams maybe used instead of cells and, as such, it is important to note that theconcepts described herein are equally applicable to both cells andbeams.

There currently exist certain challenge(s). While existing LTEMultimedia Broadcast Multicast Service (MBMS) support broadcasttransmission by means of Multimedia Broadcast Multicast Service SingleFrequency Network (MBSFN) and Single Cell Point to Multipoint (SC-PTM),there exist several limitations. Specifically, in MBSFN, multipletime-synchronized base stations transmit the same content in the sameresource block over a larger area. The system capacity of MBSFN islimited by the user spatial distribution, especially by the channelquality of the worst-case user. Alternatively, in SC-PTM, data isbroadcasted on a per-cell basis using Physical Downlink Shared Channel(PDSCH); however, the performance is subject to inter-cell interference.It may be desirable to further simplify the complex radio session setupprocedure of

LTE MBMS and tailor to 5G NR for enabling fast RAN sessions andefficient utilization of resources.

In traditional cellular systems, the antennas are concentrated atspecific locations of base stations operating at lower carrierfrequencies. In the emerging Distributed massive Multiple Input MultipleOutput (D-maMIMO) networks, each user is served by multiple antennapoints, and the link quality of distributed MIMO is relatively robust toblockings (i.e., the probability of all links suffered by blocking isless due to macro diversity). Thus, D-maMIMO would be suitable foroperating at both micro- and millimeter-wave carrier frequencies.However, no effective solutions exist for broadcasting of data contentin such cell-free/distributed networks.

One approach is to employ omnidirectional transmission at each antennapoint, which can serve as baseline for the solution proposed in thepresent disclosure.

Certain aspects of the present disclosure and their embodiments mayprovide solutions to the aforementioned or other challenges. In someembodiments, long-term Channel State Information (CSI) is utilized atthe Access Points (Aps). Based on this information:

-   -   users (i.e., wireless devices or UEs) receiving multicast or        broadcast (multicast/broadcast) information are associated with        the APs; and/or    -   precoders are determined for each AP based on said long-term CSI        for users receiving the same multicast/broadcast information.

In some embodiments, an iterative process is provided for calculatingthe precoder weights across APs. In each iteration, the precoder weightsacross APs are calculated based on the average Signal to Interferenceplus Noise Ratio (SINR) of the worst-case user (i.e., worst-casewireless device or UE).

In some embodiments, a distributed cell-free massive MIMO network isprovided. The distributed cell-free massive MIMO network comprises atleast two APs, at least one UE, and a Central Processing Unit (CPU). TheCPU may also be referred to herein as a Central Unit (CU) or moregenerally as a “central processing system” and should be distinguishedfrom a “central processing unit” or “CPU” of a computer architecture(e.g., an Intel CPU such as, e.g., an Intel i3, i5, or i7 CPU), whichmay be referred to herein as a “computer CPU” or “computer centralprocessing unit”. Each AP obtains long-term CSI (e.g., based on thefiltering or processing of uplink measurements), which is communicatedto the CPU. At the CPU, an (e.g., iterative) optimization is performedto compute a precoding vector across all the APs. Each AP thenbroadcasts the precoded multicast transmission to the UEs requiring thesame content. Further, in some embodiments, the statistical channelinformation between APs and UEs either comprises or consists ofestimated path losses. By leveraging the signals transmitted andreceived during initial access (i.e., before Radio Resource Control(RRC) connection establishment), the AP estimates the long-term CSI withrespect to the UEs within its coverage region. For instance, each UEtransmits a Physical Random Access Channel (PRACH) preamble in theuplink, which could be used to estimate the link average Signal to NoiseRatio (SNR), equivalently, the long-term channel gain between AP and UE.Since the large-scale channel coherent interval spans over severalthousands of symbols, in some embodiments, the network could schedulededicated pilot in the uplink and estimate the long-term CSI reliably infixed intervals of time.

In some embodiments, the long-term CSI available at each AP istransferred to a central cloud, where the iterative algorithm isperformed and then precoding weights are communicated to the APs foraffecting them before data transmission. In other words, the CPU may beimplemented “in the cloud”.

Certain embodiments may provide one or more of the following technicaladvantage(s). Embodiments of the present disclosure provide an efficientway of on-demand data broadcasting in distributed or cell-free massiveMIMO. The reliability of the broadcasting can be vastly improved.Embodiments of the present disclosure provide abroadcasting/multicasting scheme suitable for operating at both microand millimeter-wave frequencies.

FIG. 5 illustrates one example of a D-maMIMO system 500 in whichembodiments of the present disclosure may be implemented. Asillustrated, the D-maMIMO system 500 includes multiple APs 502-1 through502-N (generally referred to herein collectively as APs 502 andindividually as AP 502), which are geographically spread out over anarea (e.g., a large area), e.g., in a well-planned or random fashion.The APs 502 are connected to a central processing system 504 (e.g., aCPU) via corresponding backhaul links (e.g., high-capacity backhaullinks such as, e.g., fiber optic cables). The D-maMIMO system 500 isalso known as a cell-free massive MIMO system. The APs 502 provide radioaccess to a number of UEs 506. In some embodiments, the D-maMIMO system500 is or is part of a cellular communications system such as, e.g., a5G NR system.

Now, a description will be provided of some embodiments of the presentdisclosure.

Consider a distributed massive MIMO system (e.g., the D-maMIMO system500 of FIG. 5), where the APs (e.g., the APs 502) and the users (e.g.,the UEs 506) feature single or multiple antennas. Each AP serves the Kstrongest users within a certain range, say R_(c). Then, each user isserved by multiple APs uniformly distributed within the range of R_(c).For example, in the typical transmission, denote by M the number of APsserving the user under consideration, indexed by k. For the exposition,an embodiment of the present disclosure is presented first in terms ofsingle-antenna APs and single-antenna users and then generalizes to amulti-antenna case. Let us assume the correspondingprecoding/beamforming vector across the APs as w=[w₁, w₂, . . . ,w_(m)]^(T), where W_(m) is the precoding weight applied at the mth APserving the user such that ∥w∥=1.

The signal observation at the kth user is

y _(k) =H _(k) wx+n _(k)

where x is the transmit signal with power constraint E[x_(k)x*_(k)]=Pand n_(k) is the aggregate interference and noise with covarianceΣ_(k)=E[n_(k)n*_(k)].

The APs have only the knowledge of large-scale fading coefficients, butnot small-scale fading coefficients, which are essentially used toobtain the correlations as

Θ_(k) =E[H* _(k)Σ_(k) ⁻¹ H _(k) ]k=1, . . . , K

each of which is estimated by the AP during connection establishment. Inthe context of distributed massive MIMO, there are relatively largerspacings across antennas or APs. For instance, if we consider 2gigahertz (GHz) carrier frequency or wavelength λ=0.15 m, then theantenna spacing lies in the range of [100λ, 200λ] for inter-AP distances[15, 30] m. For such larger antenna spacings, the antennas becomeindependent and the correlation coefficient drops to zero, whichconsequently gives the diagonal correlation matrix whose diagonalentries are essentially reflected by the pathloss and shadowing betweenAPs and the user. While the channel matrices vary on faster time scaledue to small-scale fading, the correlations Θ_(k), k=1, . . . , K varyon a relatively slower time scale.

The local-average SINR at the kth user is

γ_(k) =E[∥Σ _(k) ^(−1/2) H _(k) wx∥ ² ]=PE[w*H* _(k)Σ_(k) ⁻¹ H _(k)w]=Pw*Θ _(k) w

which is the metric used to optimize the precoder w, rather than theinstantaneous SINRs, which are subject to fast fading whose knowledge isnot available at the APs.

Base line precoder is

$w = {{\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}.}$

Alternatively, the precoding vector is optimized iteratively such that,in each iteration w, the precoding vector is steered to maximize theaverage SINR of the worst-case user by leveraging the gradient of theaverage SINRs as Θ_(k)w, k=1, . . . , K. The steps of the process are asillustrated in FIG. 8 and described as follows. Note that these stepsare preferably performed by the CPU (e.g., the central processing system504).

-   -   Step 800: Initialize

${{w(0)} = {\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}};$

-   -   Step 802: Compute the local-average SINRs of users as        γ_(k)=Pw*Θ_(k)w, k=1, . . . , K, by means of Θ_(k);

${k^{\prime} = {\underset{{k = 1},\ldots,K}{\arg\min}\gamma_{k}}};$

-   -   Step 804: Identify the weakest user, whose index is k′ such that    -   Step 806: At iteration n+1, w(n+1)=(I+μΘ_(k′))w(n);    -   Step 808: Normalize precoder as w(n+1)=w(n+1)/∥w(n+1)∥;    -   Step 210: Repeat steps 802-808 until convergence or for a fixed        number of iterations set a priori based on the convergence        criteria.

Note that the mathematical formulas in the steps above are onlyexamples. Variations will be apparent to those of skill in the art. Forexample, one variation is that if all the antennas are collocated at onebase station or access point, then this precoding computation algorithmcan be performed at the access point itself without communicating to theCPU. As another example, one could also perform in between these twovariations. And, one could also utilize a different iterative algorithmby formulating different objective criteria, for e.g., to maximize theaverage SINR of the high priority user while guaranteeing the minimumSINR for the other users by assigning priorities to the users, insteadof maximizing the worst-case user average SINR at each iteration.

The performance of the proposed process is evaluated by the numericalresults presented below.

Evaluation methodology: Consider a live event situated in a city centerof radius 500 meters (m), where K users are requesting the same data.These K users are randomly located. We assume that M=64 APs areuniformly distributed in random locations in a circular region of radius750 m surrounding this live event. All the APs are connected to a CPUvia high speed link. To reflect the realistic interference environment,we extend the network to the radius of 2 kilometers (km), such that theinterfering APs are distributed with the same density. We simulated 5000such network realizations to gather statistics.

The cumulative distribution of the local-average SINRs are compared inFIG. 6 for baseline precoder and iteratively optimized precoder, bothwith K=4. As indicated in FIG. 6, the gain in the local-average SINRcorresponding to the worst 10% point is around 8.4 decibels (dB), whichis significant.

To verify the performance advantages of the proposed method, we repeatedthe experiment with APs placed in fixed locations according to hexagonallayout with inter-AP distance set to 200 m. This gives around M=55 APsin the circular region of 750 m. The corresponding CumulativeProbability Distribution Functions (CDFs) of local-average SINRs arecontrasted in FIG. 7. As can be seen in FIG. 7, the performanceadvantage of the proposed precoder is significant.

Shown in Table 1 is the gain in local-average SINR for M=64 and varyingK at 10-percentile point of the CDF by the iteratively optimizedprecoder over the baseline precoder.

TABLE 1 Gain in the average SINR achieved by the iteratively optimizedprecoder with respect to the baseline precoder. Gain in local-averageSINR at 10% point of the Gain in local-average CDF with APs located SINRat 10% point of the randomly in each NW CDF with APs placed in Krealization fixed hexagonal layout 1 13 dB 14.8 dB 2 10.6 dB 12.7 dB 48.4 dB 10.1 dB 8 6.5 dB 8 dB 12 5.6 dB 6.9 dB 16 5.1 dB 6.1 dB 50 3.3 dB3.7 dB

In some embodiments, the network falls back to using the “base-lineprecoder” (or some other precoder not utilizing average CSI) when thenumber of multicast users exceed a predefined threshold.

In some embodiments, the network utilizes a unicast transmission formatfor multicast services in case the number of users receiving saidmulticast service is small (e.g., below a threshold).

FIG. 9 illustrates the operation of the D-maMIMO system 500 of FIG. 5 inthe context of a 5G NR access network in accordance with someembodiments of the present disclosure. As illustrated, the centralprocessing system 504 configures uplink measurements for the UEs 506-1through 506-K (denoted here as UE-1 to UE-K) in the vicinity of the APs502-1 through 502-M (denoted here as AP-1 through AP-M) (step 900). UE-1through UE-K transmit uplink transmissions (step 902), which arereceived by AP-1 through AP-K.

Each AP (denoted here as AP-m, where m=1, M) performs uplinkmeasurements on the received uplink transmissions, processes its uplinkmeasurements to acquire long-term CSI estimates for UE-1 through UE-K,and sends the acquired long-term CSI estimates to the central processingsystem 504 (step 904). Note that Θ_(k) is computed based on thelong-term CSI estimates, which can be interpreted as the covariance ofchannel estimates.

A UE-to-AP association decision is performed (step 906). UE-to-APassociation can be autonomous across APs. For e.g., each UE isassociated with the APs that provide higher received signal power (RSRP)(e.g., higher than a certain RSRP threshold) in the downlink. This ispart of the connection establishment process or initial access of thenetwork. Each AP aims to serve the UEs associated with it.

The central processing system 504 uses the long-term CSI estimatesreceived from the APs (AP-1 through AP-K) to compute a precoding weightw for broadcasting/multicasting data to the UEs (UE-1 through UE-K)(step 908). More specifically, the central processing system 504 usesthe process described above (see, e.g., steps 800-810 of FIG. 8described above) to compute the precoding weight w to be applied acrossthe APs for broadcasting/multicasting data to the UEs (UE-1 throughUE-K).

The central processing system 504 provides the computed precoding weightw to the APs. In this manner, the APs obtain the precoding weight w fromthe central processing system 504 (step 910). Note that each AP onlyobtains the precoder weights for the UEs that are associated with thatAP (via the UE-to-AP association decision). The APs then precode data tobe multicast/broadcast to the UEs (UE-1 through UE-K) using theprecoding weight w and multicast/broadcast the resulting precoded data(step 9712).

This process may be repeated, e.g., in fixed intervals of, e.g., 100milliseconds depending on the traffic variations (step 914). Note thatthis interval may be based on the “time horizon,” which is the time overwhich the conditions for the algorithm are to be stable. For example, itis the time after which Θ_(k)=E[H*_(k)Σ_(k) ⁻¹H_(k)] has changedsignificantly and the algorithm needs to be updated. The value of 100 msfor the interval is a good example of a typical value, but it dependson, e.g., the frequency band (higher frequencies require more frequentupdates), the UE speed (high UE speed requires more frequent updates),and on the speed of other objects in the environment. In a verystationary environment, an update periodicity of 1 second might be goodenough.

Note that description above is presented in terms of single-antenna APsand single-antenna users but generalizes to a multi-antenna case. Forexample, if the APs feature multiple antennas, then the scalar precodingweights wm becomes precoding vectors of dimension equal to the number ofantennas at the APs and a similar procedure can be applied as in thecase of single-antenna APs.

FIG. 10 is a schematic block diagram of an AP 502 according to someembodiments of the present disclosure. As illustrated, the AP 502includes a control system 1002 that includes one or more processors 1004(e.g., CPUs, Application Specific Integrated Circuits (ASICs), FieldProgrammable Gate Arrays (FPGAs), and/or the like), memory 1006, and anetwork interface 1008. The one or more processors 1004 are alsoreferred to herein as processing circuitry. In addition, the AP 502includes one or more radio units 1010 that each includes one or moretransmitters 1012 and one or more receivers 1014 coupled to one or moreantennas 1016. The radio units 1010 may be referred to or be part ofradio interface circuitry. In some embodiments, the radio unit(s) 1010is external to the control system 1002 and connected to the controlsystem 1002 via, e.g., a wired connection (e.g., an optical cable).However, in some other embodiments, the radio unit(s) 1010 andpotentially the antenna(s) 1016 are integrated together with the controlsystem 1002. The one or more processors 1004 operate to provide one ormore functions of the AP 502 as described herein. In some embodiments,the function(s) are implemented in software that is stored, e.g., in thememory 1006 and executed by the one or more processors 1004.

In some embodiments, a computer program including instructions which,when executed by at least one processor, causes the at least oneprocessor to carry out the functionality of the AP 502 according to anyof the embodiments described herein is provided. In some embodiments, acarrier comprising the aforementioned computer program product isprovided. The carrier is one of an electronic signal, an optical signal,a radio signal, or a computer readable storage medium (e.g., anon-transitory computer readable medium such as memory).

FIG. 11 is a schematic block diagram of the AP 502 according to someother embodiments of the present disclosure. The AP 502 includes one ormore modules 1100, each of which is implemented in software. Themodule(s) 1100 provide the functionality of AP 502 described herein.

FIG. 12 is a schematic block diagram of the central processing system504 according to some embodiments of the present disclosure. Asillustrated, the central processing system 504 includes one or moreprocessors 1204 (e.g., CPUs, ASICs, FPGAs, and/or the like), memory1206, and a network interface 1208. The one or more processors 1204 arealso referred to herein as processing circuitry. The one or moreprocessors 1204 operate to provide one or more functions of the centralprocessing system 504 as described herein. In some embodiments, thefunction(s) are implemented in software that is stored, e.g., in thememory 1206 and executed by the one or more processors 1204.

FIG. 13 is a schematic block diagram that illustrates a virtualizedembodiment of the central processing system 504 according to someembodiments of the present disclosure. As used herein, a “virtualized”central processing system 504 is an implementation of the centralprocessing system 504 in which at least a portion of the functionalityof the central processing system 504 is implemented as a virtualcomponent(s) (e.g., via a virtual machine(s) executing on a physicalprocessing node(s) in a network(s)). As illustrated, in this example,the central processing system 504 includes one or more processing nodes1300 coupled to or included as part of a network(s) 1302. Eachprocessing node 1300 includes one or more processors 1304 (e.g., CPUs,ASICs, FPGAs, and/or the like), memory 1306, and a network interface1308.

In this example, functions 1310 of the central processing system 504described herein are implemented at the one or more processing nodes1300. In some particular embodiments, some or all of the functions 1310of the central processing system 504 described herein are implemented asvirtual components executed by one or more virtual machines implementedin a virtual environment(s) hosted by the processing node(s) 1300.

In some embodiments, a computer program including instructions which,when executed by at least one processor, causes the at least oneprocessor to carry out the functionality of the central processingsystem 504 or a node (e.g., a processing node 1300) implementing one ormore of the functions 1310 of the central processing system 504 in avirtual environment according to any of the embodiments described hereinis provided. In some embodiments, a carrier comprising theaforementioned computer program product is provided. The carrier is oneof an electronic signal, an optical signal, a radio signal, or acomputer readable storage medium (e.g., a non-transitory computerreadable medium such as memory).

FIG. 14 is a schematic block diagram of the central processing system504 according to some other embodiments of the present disclosure. Thecentral processing system 504 includes one or more modules 1400, each ofwhich is implemented in software. The module(s) 1400 provide thefunctionality of the central processing system 504 described herein.This discussion is equally applicable to the processing node 1300 ofFIG. 13 where the modules 1400 may be implemented at one of theprocessing nodes 1300 or distributed across multiple processing nodes1300.

With reference to FIG. 15, in accordance with an embodiment, acommunication system includes a telecommunication network 1500, such asa 3GPP-type cellular network, which comprises an access network 1502,such as a RAN, and a core network 1504. The access network 1502comprises a plurality of base stations 1506A, 1506B, 1506C, such as NodeBs, eNBs, gNBs, or other types of wireless APs, each defining acorresponding coverage area 1508A, 1508B, 1508C. Each base station1506A, 1506B, 1506C is connectable to the core network 1504 over a wiredor wireless connection 1510. A first UE 1512 located in coverage area1508C is configured to wirelessly connect to, or be paged by, thecorresponding base station 1506C. A second UE 1514 in coverage area1508A is wirelessly connectable to the corresponding base station 1506A.While a plurality of UEs 1512, 1514 are illustrated in this example, thedisclosed embodiments are equally applicable to a situation where a soleUE is in the coverage area or where a sole UE is connecting to thecorresponding base station 1506.

The telecommunication network 1500 is itself connected to a hostcomputer 1516, which may be embodied in the hardware and/or software ofa standalone server, a cloud-implemented server, a distributed server,or as processing resources in a server farm. The host computer 1516 maybe under the ownership or control of a service provider, or may beoperated by the service provider or on behalf of the service provider.Connections 1518 and 1520 between the telecommunication network 1500 andthe host computer 1516 may extend directly from the core network 1504 tothe host computer 1516 or may go via an optional intermediate network1522. The intermediate network 1522 may be one of, or a combination ofmore than one of, a public, private, or hosted network; the intermediatenetwork 1522, if any, may be a backbone network or the Internet; inparticular, the intermediate network 1522 may comprise two or moresub-networks (not shown).

The communication system of FIG. 15 as a whole enables connectivitybetween the connected UEs 1512, 1514 and the host computer 1516. Theconnectivity may be described as an Over-the-Top (OTT) connection 1524.The host computer 1516 and the connected UEs 1512, 1514 are configuredto communicate data and/or signaling via the OTT connection 1524, usingthe access network 1502, the core network 1504, any intermediate network1522, and possible further infrastructure (not shown) as intermediaries.The OTT connection 1524 may be transparent in the sense that theparticipating communication devices through which the OTT connection1524 passes are unaware of routing of uplink and downlinkcommunications. For example, the base station 1506 may not or need notbe informed about the past routing of an incoming downlink communicationwith data originating from the host computer 1516 to be forwarded (e.g.,handed over) to a connected UE 1512. Similarly, the base station 1506need not be aware of the future routing of an outgoing uplinkcommunication originating from the UE 1512 towards the host computer1516.

Example implementations, in accordance with an embodiment, of the UE,base station, and host computer discussed in the preceding paragraphswill now be described with reference to FIG. 16. In a communicationsystem 1600, a host computer 1602 comprises hardware 1604 including acommunication interface 1606 configured to set up and maintain a wiredor wireless connection with an interface of a different communicationdevice of the communication system 1600. The host computer 1602 furthercomprises processing circuitry 1608, which may have storage and/orprocessing capabilities. In particular, the processing circuitry 1608may comprise one or more programmable processors, ASICs, FPGAs, orcombinations of these (not shown) adapted to execute instructions. Thehost computer 1602 further comprises software 1610, which is stored inor accessible by the host computer 1602 and executable by the processingcircuitry 1608. The software 1610 includes a host application 1612. Thehost application 1612 may be operable to provide a service to a remoteuser, such as a UE 1614 connecting via an OTT connection 1616terminating at the UE 1614 and the host computer 1602. In providing theservice to the remote user, the host application 1612 may provide userdata which is transmitted using the OTT connection 1616.

The communication system 1600 further includes a base station 1618provided in a telecommunication system and comprising hardware 1620enabling it to communicate with the host computer 1602 and with the UE1614. The hardware 1620 may include a communication interface 1622 forsetting up and maintaining a wired or wireless connection with aninterface of a different communication device of the communicationsystem 1600, as well as a radio interface 1624 for setting up andmaintaining at least a wireless connection 1626 with the UE 1614 locatedin a coverage area (not shown in FIG. 16) served by the base station1618. The communication interface 1622 may be configured to facilitate aconnection 1628 to the host computer 1602. The connection 1628 may bedirect or it may pass through a core network (not shown in FIG. 16) ofthe telecommunication system and/or through one or more intermediatenetworks outside the telecommunication system. In the embodiment shown,the hardware 1620 of the base station 1618 further includes processingcircuitry 1630, which may comprise one or more programmable processors,ASICs, FPGAs, or combinations of these (not shown) adapted to executeinstructions. The base station 1618 further has software 1632 storedinternally or accessible via an external connection.

The communication system 1600 further includes the UE 1614 alreadyreferred to. The UE's 1614 hardware 1634 may include a radio interface1636 configured to set up and maintain a wireless connection 1626 with abase station serving a coverage area in which the UE 1614 is currentlylocated. The hardware 1634 of the UE 1614 further includes processingcircuitry 1638, which may comprise one or more programmable processors,ASICs, FPGAs, or combinations of these (not shown) adapted to executeinstructions. The UE 1614 further comprises software 1640, which isstored in or accessible by the UE 1614 and executable by the processingcircuitry 1638. The software 1640 includes a client application 1642.The client application 1642 may be operable to provide a service to ahuman or non-human user via the UE 1614, with the support of the hostcomputer 1602. In the host computer 1602, the executing host application1612 may communicate with the executing client application 1642 via theOTT connection 1616 terminating at the UE 1614 and the host computer1602. In providing the service to the user, the client application 1642may receive request data from the host application 1612 and provide userdata in response to the request data. The OTT connection 1616 maytransfer both the request data and the user data. The client application1642 may interact with the user to generate the user data that itprovides.

It is noted that the host computer 1602, the base station 1618, and theUE 1614 illustrated in FIG. 16 may be similar or identical to the hostcomputer 1516, one of the base stations 1506A, 1506B, 1506C, and one ofthe UEs 1512, 1514 of FIG. 15, respectively. This is to say, the innerworkings of these entities may be as shown in FIG. 16 and independently,the surrounding network topology may be that of FIG. 15.

In FIG. 16, the OTT connection 1616 has been drawn abstractly toillustrate the communication between the host computer 1602 and the UE1614 via the base station 1618 without explicit reference to anyintermediary devices and the precise routing of messages via thesedevices. The network infrastructure may determine the routing, which maybe configured to hide from the UE 1614 or from the service provideroperating the host computer 1602, or both. While the OTT connection 1616is active, the network infrastructure may further take decisions bywhich it dynamically changes the routing (e.g., on the basis of loadbalancing consideration or reconfiguration of the network).

The wireless connection 1626 between the UE 1614 and the base station1618 is in accordance with the teachings of the embodiments describedthroughout this disclosure. One or more of the various embodimentsimprove the performance of OTT services provided to the UE 1614 usingthe OTT connection 1616, in which the wireless connection 1626 forms thelast segment.

A measurement procedure may be provided for the purpose of monitoringdata rate, latency, and other factors on which the one or moreembodiments improve. There may further be an optional networkfunctionality for reconfiguring the OTT connection 1616 between the hostcomputer 1602 and the UE 1614, in response to variations in themeasurement results. The measurement procedure and/or the networkfunctionality for reconfiguring the OTT connection 1616 may beimplemented in the software 1610 and the hardware 1604 of the hostcomputer 1602 or in the software 1640 and the hardware 1634 of the UE1614, or both. In some embodiments, sensors (not shown) may be deployedin or in association with communication devices through which the OTTconnection 1616 passes; the sensors may participate in the measurementprocedure by supplying values of the monitored quantities exemplifiedabove, or supplying values of other physical quantities from which thesoftware 1610, 1640 may compute or estimate the monitored quantities.The reconfiguring of the OTT connection 1616 may include message format,retransmission settings, preferred routing, etc.; the reconfiguring neednot affect the base station 1618, and it may be unknown or imperceptibleto the base station 1618. Such procedures and functionalities may beknown and practiced in the art. In certain embodiments, measurements mayinvolve proprietary UE signaling facilitating the host computer 1602'smeasurements of throughput, propagation times, latency, and the like.The measurements may be implemented in that the software 1610 and 1640causes messages to be transmitted, in particular empty or ‘dummy’messages, using the OTT connection 1616 while it monitors propagationtimes, errors, etc.

FIG. 17 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station, and a UEwhich may be those described with reference to FIGS. 15 and 16. Forsimplicity of the present disclosure, only drawing references to FIG. 17will be included in this section. In step 1700, the host computerprovides user data. In sub-step 1702 (which may be optional) of step1700, the host computer provides the user data by executing a hostapplication. In step 1704, the host computer initiates a transmissioncarrying the user data to the UE. In step 1706 (which may be optional),the base station transmits to the UE the user data which was carried inthe transmission that the host computer initiated, in accordance withthe teachings of the embodiments described throughout this disclosure.In step 1708 (which may also be optional), the UE executes a clientapplication associated with the host application executed by the hostcomputer.

FIG. 18 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station, and a UEwhich may be those described with reference to FIGS. 15 and 16. Forsimplicity of the present disclosure, only drawing references to FIG. 18will be included in this section. In step 1800 of the method, the hostcomputer provides user data. In an optional sub-step (not shown) thehost computer provides the user data by executing a host application. Instep 1802, the host computer initiates a transmission carrying the userdata to the UE. The transmission may pass via the base station, inaccordance with the teachings of the embodiments described throughoutthis disclosure. In step 1804 (which may be optional), the UE receivesthe user data carried in the transmission.

FIG. 19 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station, and a UEwhich may be those described with reference to FIGS. 15 and 16. Forsimplicity of the present disclosure, only drawing references to FIG. 19will be included in this section. In step 1900 (which may be optional),the UE receives input data provided by the host computer. Additionallyor alternatively, in step 1902, the UE provides user data. In sub-step1904 (which may be optional) of step 1900, the UE provides the user databy executing a client application. In sub-step 1906 (which may beoptional) of step 1902, the UE executes a client application whichprovides the user data in reaction to the received input data providedby the host computer. In providing the user data, the executed clientapplication may further consider user input received from the user.Regardless of the specific manner in which the user data was provided,the UE initiates, in sub-step 1908 (which may be optional), transmissionof the user data to the host computer. In step 1910 of the method, thehost computer receives the user data transmitted from the UE, inaccordance with the teachings of the embodiments described throughoutthis disclosure.

FIG. 20 is a flowchart illustrating a method implemented in acommunication system, in accordance with one embodiment. Thecommunication system includes a host computer, a base station, and a UEwhich may be those described with reference to FIGS. 15 and 16. Forsimplicity of the present disclosure, only drawing references to FIG. 20will be included in this section. In step 2000 (which may be optional),in accordance with the teachings of the embodiments described throughoutthis disclosure, the base station receives user data from the UE. Instep 2002 (which may be optional), the base station initiatestransmission of the received user data to the host computer. In step2004 (which may be optional), the host computer receives the user datacarried in the transmission initiated by the base station.

Any appropriate steps, methods, features, functions, or benefitsdisclosed herein may be performed through one or more functional unitsor modules of one or more virtual apparatuses. Each virtual apparatusmay comprise a number of these functional units. These functional unitsmay be implemented via processing circuitry, which may include one ormore microprocessor or microcontrollers, as well as other digitalhardware, which may include Digital Signal Processor (DSPs),special-purpose digital logic, and the like. The processing circuitrymay be configured to execute program code stored in memory, which mayinclude one or several types of memory such as Read Only Memory (ROM),Random Access Memory (RAM), cache memory, flash memory devices, opticalstorage devices, etc. Program code stored in memory includes programinstructions for executing one or more telecommunications and/or datacommunications protocols as well as instructions for carrying out one ormore of the techniques described herein. In some implementations, theprocessing circuitry may be used to cause the respective functional unitto perform corresponding functions according one or more embodiments ofthe present disclosure.

While processes in the figures may show a particular order of operationsperformed by certain embodiments of the present disclosure, it should beunderstood that such order is exemplary (e.g., alternative embodimentsmay perform the operations in a different order, combine certainoperations, overlap certain operations, etc.).

Some example embodiments of the present disclosure are as follows.

Group A Embodiments

Embodiment 1: A method for broadcasting or multicasting data to UserEquipments, UEs, in a distributed cell-free massive Multiple InputMultiple Output, MIMO, network, comprising any one or more of thefollowing actions:

-   -   at each Access Point, AP, (502-m) of two or more APs (502-1, . .        . , 502-M):        -   obtaining (904) long-term Channel State Information, CSI,            for at least one UE (506); and        -   communicating (904) the long-term CSI for the at least one            UE (506) to a central processing system (504);    -   at the central processing system (504):        -   for each AP (502-m) of the two or more APs (502-1, . . . ,            502-M), receiving (904) the long-term CSI for the at least            one UE (506) from the AP (502-m);        -   computing (908) a precoding vector, w, for the at least one            UE (506) across the two or more APs (502-1, . . . , 502-M)            based on the long-term CSI for the at least one UE (506)            received from the two or more APs (502-1, . . . , 502-M);            and        -   communicating (910) the precoding vector, w, to the two or            more APs (502-1, . . . , 502-M); and    -   at each AP (502-m) of the two or more APs (502-1, . . . ,        502-M):        -   obtaining (910) the precoding vector, w, from the central            processing system (504);        -   precoding (912) data to be broadcast or multicast to the at            least one UE (506) based on the precoding vector, w; and        -   broadcasting or multicasting (912) the precoded data to the            at least one UE (506).

Embodiment 2: The method of embodiment 1 wherein the at least one UE(506) is two or more UEs (506-1, . . . , 506-K).

Embodiment 3: The method of embodiment 1 or 2 wherein computing (908)the precoding vector, w, comprises:

-   -   a) at iteration 0, initializing the precoding vector, w, to        provide a precoding vector w(0) for iteration 0;    -   b) computing local-average Signal to Interference plus Noise        Ratio, SINRs, of the at least one UE (506);    -   c) identifying a weakest UE from among the at least one UE (506)        based on the computed local-average SINRs;    -   d) at iteration n+1, updating the precoding vector, w, based on        the long-term CSI obtained from the at least one UE (506) for        the weakest UE to thereby provide a precoding vector w(n+1) for        iteration n+1;    -   e) normalizing the precoding vector w(n+1) for iteration n+1;        and    -   f) repeating steps (b) through (e) until a stopping criterion is        satisfied (e.g., convergence or maximum number of iterations has        been reached) such that the normalized precoding vector for the        last iteration is provided as the precoding vector, w.

Embodiment 4: The method of embodiment 3 wherein initializing theprecoding vector, w, comprises initializing the precoding vector, w, toa value

${w(0)} = {{\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}.}$

Embodiment 5: The method of embodiment 3 or 4 wherein computing thelocal SINRs of the at least one UE (506) comprises computing the localSINRs of the at least one UE (506) as γ_(k)=Pw*Θ_(k)w, k=1, . . . ,K, bymeans of Θ_(k).

Embodiment 6: The method of embodiment 5 wherein identifying the weakestUE as the UE whose index k′ is

$k^{\prime} = {\underset{{k = 1},\ldots,K}{\arg\min}{\gamma_{k}.}}$

Embodiment 7: The method of any one of embodiments 3 to 6 whereinupdating the precoding vector, w, comprises updating the precodingvector, w, at iteration n+1 as w(n+1)=(I+μΘ_(k′))w(n).

Embodiment 8: The method of any one of embodiments 3 to 7 whereinnormalizing the precoding vector w(n+1) for iteration n+1 comprisesnormalizing the precoding vector w(n+1) for iteration n+1 asw(n+1)=w(n+1)/∥w(n+1)∥.

Embodiment 9: The method of any one of embodiments 1 to 8 wherein thelong-term CSI comprises estimated path loss.

Embodiment 10: The method of any one of embodiments 1 to 9 wherein acoherent interval of the long-term CSI spans greater than 1,000 symbols,and the method further comprises: at the central processing system(504), scheduling transmissions by the at least one UE (506) of adedicated pilot; and at each AP (502-m) of the two or more APs (502-1, .. . , 502-M), obtaining (904) the long-term CSI for the at least one UE(506) comprises estimating the long-term CSI for the at least one UE(506) in fixed intervals of time, based on the transmissions of thededicated pilot.

Embodiment 11: A method performed at a central processing system (504)for a distributed cell-free massive MIMO network for broadcasting ormulticasting data to User Equipments, UEs, comprising any one or more ofthe following actions:

-   -   for each Access Point, AP, (502-m) of two or more APs (502-1, .        . . , 502-M) in the distributed cell-free massive Multiple Input        Multiple Output, MIMO, network, receiving (904) long-term        Channel State Information, CSI, for at least one UE (506) from        the AP (502-m);    -   computing (908) a precoding vector, w, for the at least one UE        (506) across all of the two or more APs (502-1, . . . , 502-M)        based on the long-term CSI for the at least one UE (506)        received from the two or more APs (502-1, . . . , 502-M); and        communicating (910) the precoding vector, w, to the two or more        APs (502-1, . . . , 502-M).

Embodiment 12: The method of embodiment 11 wherein the at least one UE(506) is two or more UEs (506-1, . . . , 506-K).

Embodiment 13: The method of embodiment 11 or 12 wherein computing (908)the precoding vector, w, comprises:

-   -   a) at iteration 0, initializing the precoding vector, w, to        provide a precoding vector w(0) for iteration 0;    -   b) computing local-average Signal to Interference plus Noise        Ratios, SINRs, of the at least one UE (506);    -   c) identifying a weakest UE from among the at least one UE (506)        based on the computed local-average SINRs;    -   d) at iteration n+1, updating the precoding vector, w, based on        the long-term CSI obtained from the at least one UE (506) for        the weakest UE to thereby provide a precoding vector w(n+1) for        iteration n+1;    -   e) normalizing the precoding vector w(n+1) for iteration n+1;        and    -   f) repeating steps (b) through (e) until a stopping criterion is        satisfied (e.g., convergence or maximum number of iterations has        been reached) such that the normalized precoding vector for the        last iteration is provided as the precoding vector, w.

Embodiment 14: The method of embodiment 13 wherein initializing theprecoding vector, w, comprises initializing the precoding vector, w, toa value

${w(0)} = {{\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}.}$

Embodiment 15: The method of embodiment 13 or 14 wherein computing thelocal SINRs of the at least one UE (506) comprises computing the localSINRs of the at least one UE (506) as γ_(k)=Pw*Θ_(k)w, k=1, by means ofΘ_(k).

Embodiment 16: The method of embodiment 15 wherein identifying theweakest UE as the UE whose index k′ is

$k^{\prime} = {\underset{{k = 1},\ldots,K}{\arg\min}{\gamma_{k}.}}$

Embodiment 17: The method of any one of embodiments 13 to 16 whereinupdating the precoding vector, w, comprises updating the precodingvector, w, at iteration n+1 as w(n+1)=(1+μΘ_(k′))w(n).

Embodiment 18: The method of any one of embodiments 13 to 17 whereinnormalizing the precoding vector w(n+1) for iteration n+1 comprisesnormalizing the precoding vector w(n+1) for iteration n+1 asw(n+1)=w(n+1)/∥w(n+1)∥.

Embodiment 19: The method of any one of embodiments 11 to 18 wherein thelong-term CSI comprises estimated path loss.

Embodiment 20: The method of any one of embodiments 11 to 19 wherein acoherent interval of the long-term CSI spans greater than 1,000 symbols,and the method further comprises scheduling transmissions by the atleast one UE (506) of a dedicated pilot.

Embodiment 21: A method performed at an Access Point, AP, in adistributed cell-free massive Multiple Input Multiple Output, MIMO,network for broadcasting or multi-casting data to User Equipments, UEs,the network comprising two or more APs, and the method comprising anyone or more of the following actions:

-   -   obtaining (904) long-term Channel State Information, CSI, for at        least one UE (506);    -   communicating (904) the long-term CSI for the at least one UE        (506) to a central processing system (504);    -   obtaining (910) a precoding vector, w, from the central        processing system (504), the precoding vector, w, being for the        at least one UE (506) across all of two or more APs (502-1, . .        . , 502-M);

precoding (912) data to be broadcast or multicast to the at least one UE(506) based on the precoding vector, w; and broadcasting or multicasting(912) the precoded data to the at least one UE (506).

Embodiment 22: The method of embodiment 21 wherein the at least one UE(506) is two or more UEs (506-1, . . . , 506-K).

Embodiment 23: The method of any one of embodiments 21 to 22 wherein thelong-term CSI comprises estimated path loss.

Embodiment 24: The method of any one of embodiments 21 to 23 wherein acoherent interval of the long-term CSI spans greater than 1,000 symbols,and obtaining (904) the long-term CSI for the at least one UE (506)comprises estimating the long-term CSI for the at least one UE (506) infixed intervals of time, based on transmissions of a dedicated pilot bythe at least one UE (506).

Embodiment 25: A central processing system for a distributed cell-freemassive Multiple Input Multiple Output, MIMO, network for broadcastingor multicasting data to User Equipments, UEs, wherein the centralprocessing system is adapted to perform the method of any one ofembodiments 11 to 20.

Embodiment 26: The central processing system of embodiment 25comprising: a network interface; and processing circuitry associatedwith the network interface, the processing circuitry configured to causethe central processing system to perform the method of any one ofembodiments 11 to 20.

Embodiment 27: An Access Point, AP, for a distributed cell-free massiveMultiple Input Multiple Output, MIMO, network for broadcasting ormulticasting data to User Equipments, UEs, the distributed cell-freemassive MIMO network comprising two or more APs, and the AP adapted toperform the method of any one of embodiments 21 to 24.

Embodiment 28: The AP of embodiment 27 comprising: a network interface;at least one transmitter; at least one receiver; and processingcircuitry associated with the network interface, the at least onetransmitter, and the at least one receiver, wherein the processingcircuitry is configured to cause the AP to perform the method of any oneof embodiments 21 to 24.

Embodiment 29: A distributed cell-free massive Multiple Input MultipleOutput, MIMO, network comprising at least two Access Points, APs, atleast one User Equipment, UE, and a Central Processing Unit, CPU. EachAP obtains long-term Channel State Information, CSI, (e.g., based onfiltering or processing of uplink measurements) which are communicatedto the CPU, where an (e.g., iterative) optimization is performed tocompute the precoding vector across all the at least two APs. Each APthen broadcasts a precoded multicast transmission to the at least one UErequiring the same content.

Embodiment 30: The method in the previous embodiment, where thestatistical channel information between Access Points, APs, and UserEquipments, UEs, consists of estimated path losses. By leveraging thesignals transmitted and received during initial access (i.e., beforeRadio Resource Control, RRC, connection establishment), AP estimates thelong-term Channel State Information, CSI, with respect to the UEs withinthe coverage region. For instance, each UE transmits a Physical RandomAccess Channel, PRACH, preamble in the uplink, which could be used toestimate the link average Signal to Noise Ratio, SNR, equivalently, thelong-term channel gain between the AP and the UE.

Embodiment 31: Since the large-scale channel coherent interval spansover several thousands of symbols, in some embodiments, a network couldschedule a dedicated pilot in the uplink and estimate the long-termChannel State Information, CSI, reliably in fixed intervals of time.

Embodiment 32: The method of any of the previous embodiments, furthercomprising: obtaining user data; and forwarding the user data to a hostcomputer or a wireless device.

Group B Embodiments

Embodiment 33: A central processing system (504) for a distributedcell-free massive

Multiple Input Multiple Output, MIMO, network for broadcasting ormulticasting data to User Equipments, UEs, comprising: processingcircuitry configured to perform any of the steps performed by thecentral processing system in any of the Group A embodiments; and powersupply circuitry configured to supply power.

Embodiment 34: A communication system including a host computercomprising:

processing circuitry configured to provide user data; and acommunication interface configured to forward the user data to acellular network for transmission to a User Equipment, UE; wherein thecommunication system comprises a central processing system (504) for adistributed cell-free massive Multiple Input Multiple Output, MIMO,network for broadcasting or multicasting data to UEs, the centralprocessing system having a network interface and processing circuitry,the central processing system's processing circuitry configured toperform any of the steps performed by the central processing system inany of the Group A embodiments.

Embodiment 35: The communication system of the previous embodimentfurther including the central processing system.

Embodiment 36: The communication system of the previous 2 embodiments,further including the UE, wherein the UE is configured to communicatewith at least one Access Point, AP, of the distributed cell-free massiveMIMO network.

Embodiment 37: The communication system of the previous 3 embodiments,wherein: the processing circuitry of the host computer is configured toexecute a host application, thereby providing the user data; and the UEcomprises processing circuitry configured to execute a clientapplication associated with the host application.

Embodiment 38: A method implemented in a communication system includinga host computer, a base station, and a User Equipment, UE, the methodcomprising: at the host computer, providing user data; and at the hostcomputer, initiating a transmission carrying the user data to the UE viaa cellular network comprising a central processing system (504) for adistributed cell-free massive Multiple Input Multiple Output, MIMO,network for broadcasting or multi-casting data to UEs, wherein thecentral processing system (504) performs any of the steps performed bythe central processing system in of any of the Group A embodiments.

Embodiment 39: The method of the previous embodiment, furthercomprising, at one or more Access Points, APs, of the distributedcell-free massive MIMO network, transmitting the user data.

Embodiment 40: The method of the previous 2 embodiments, wherein theuser data is provided at the host computer by executing a hostapplication, the method further comprising, at the UE, executing aclient application associated with the host application.

Embodiment 41: A User Equipment, UE, configured to communicate with abase station, the UE comprising a radio interface and processingcircuitry configured to perform the method of the previous 3embodiments.

At least some of the following abbreviations may be used in thisdisclosure. If there is an inconsistency between abbreviations,preference should be given to how it is used above. If listed multipletimes below, the first listing should be preferred over any subsequentlisting(s).

-   -   3GPP Third Generation Partnership Project    -   4G Fourth Generation    -   5G Fifth Generation    -   AP Access Point    -   APU Antenna Processing Unit    -   ASIC Application Specific Integrated Circuit    -   BS Base Station    -   CDF Cumulative Probability Distribution Function    -   C-maMIMO Centralized Massive Multiple Input Multiple Output    -   CPU Central Processing Unit    -   CSI Channel State Information    -   dB Decibel    -   D-maMIMO Distributed Massive Multiple Input Multiple Output    -   DSP Digital Signal Processor    -   eNB Enhanced or Evolved Node B    -   FDD Frequency Division Duplexing    -   FPGA Field Programmable Gate Array    -   GHz Gigahertz    -   gNB New Radio Base Station    -   km Kilometer    -   LED Light Emitting Diode    -   LTE Long Term Evolution    -   m Meter    -   MAC Medium Access Control    -   MBMS Multimedia Broadcast Multicast Service    -   MBSFN Multimedia Broadcast Multicast Service Single Frequency        Network    -   MIMO Multiple Input Multiple Output    -   MME Mobility Management Entity    -   MTC Machine Type Communication    -   NR New Radio    -   OTT Over-the-Top    -   PDSCH Physical Downlink Shared Channel    -   P-GW Packet Data Network Gateway    -   PRACH Physical Random Access Channel    -   RAM Random Access Memory    -   RAN Radio Access Network    -   ROM Read Only Memory    -   RRC Radio Resource Control    -   SAR Specific Absorption Rate    -   SCEF Service Capability Exposure Function    -   SC-PTM Single Cell Point to Multipoint    -   SFN Single Frequency Network    -   SINR Signal to Interference plus Noise Ratio    -   SNR Signal to Noise Ratio    -   TDD Time Division Duplexing    -   UE User Equipment

Those skilled in the art will recognize improvements and modificationsto the embodiments of the present disclosure. All such improvements andmodifications are considered within the scope of the concepts disclosedherein.

1. A method for broadcasting or multicasting data to User Equipments(UEs) in a distributed cell-free massive Multiple Input Multiple Output(MIMO) network, comprising: at each Access Point, AP, of two or moreAPs: obtaining long-term Channel State Information, CSI, for at leastone UE; and communicating the long-term CSI for the at least one UE to acentral processing system; at the central processing system: for each APof the two or more APs, receiving the long-term CSI for the at least oneUE from the AP; computing a precoding vector, w, for the at least one UEacross the two or more APs based on the long-term CSI for the at leastone UE received from the two or more APs; and communicating theprecoding vector, w, to the two or more APs; and at each AP of the twoor more APs: obtaining the precoding vector, w, from the centralprocessing system; precoding data to be broadcast or multicast to the atleast one UE based on the precoding vector, w; and broadcasting ormulticasting the precoded data to the at least one UE.
 2. A methodperformed at a central processing system for a distributed cell-freemassive Multiple Input Multiple Output, MIMO, network for broadcastingor multicasting data to User Equipments (UEs) comprising: for eachAccess Point, AP, of two or more APs in the distributed cell-freemassive MIMO network, receiving long-term Channel State Information,CSI, for at least one UE from the AP; computing a precoding vector, w,for the at least one UE across all of the two or more APs based on thelong-term CSI for the at least one UE received from the two or more APs;and communicating the precoding vector, w, to the two or more APs. 3.The method of claim Error! Reference source not found. wherein the atleast one UE is two or more UEs.
 4. The method of claim Error! Referencesource not found. wherein computing the precoding vector, w, comprises:at iteration 0, initializing the precoding vector, w, to provide aprecoding vector w(0) for iteration 0; computing local-average Signal toInterference plus Noise Ratios, SINRs, of the at least one UE;identifying a weakest UE from among the at least one UE based on thecomputed local-average SINRs; at iteration n+1, updating the precodingvector, w, based on the long-term CSI obtained from the at least one UEfor the weakest UE to thereby provide a precoding vector w(n+1) foriteration n+1; normalizing the precoding vector w(n+1) for iterationn+1; and repeating steps (b) through (e) until a stopping criterion issatisfied such that the normalized precoding vector for the lastiteration is provided as the precoding vector, w.
 5. The method of claimError! Reference source not found. wherein the stopping criterion isconvergence or maximum number of iterations has been reached.
 6. Themethod of claim Error! Reference source not found. wherein initializingthe precoding vector, w, comprises initializing the precoding vector, w,to a value${w(0)} = {{\frac{1}{\sqrt{M}}\left\lbrack {1,\ldots,1} \right\rbrack}^{T}.}$7. The method of claim Error! Reference source not found. whereincomputing the local SINRs of the at least one UE comprises computing thelocal SINRs of the at least one UE as γ_(k)=Pw*Θ_(k)w, k=1, . . . , K,by means of Θ_(k).
 8. The method of claim Error! Reference source notfound. wherein identifying the weakest UE as the UE whose index k′ is$k^{\prime} = {\underset{{k = 1},\ldots,K}{\arg\min}{\gamma_{k}.}}$ 9.The method of claim Error! Reference source not found. wherein updatingthe precoding vector, w, comprises updating the precoding vector, w, atiteration n+1 as w(n+1)=(I+μΘ_(k′))w(n).
 10. The method of claim Error!Reference source not found. wherein normalizing the precoding vectorw(n+1) for iteration n+1 comprises normalizing the precoding vectorw(n+1) for iteration n+1 as w(n+1)=w(n+1)/∥w(n+1)∥.
 11. The method ofclaim Error! Reference source not found. wherein the long-term CSIcomprises estimated path loss.
 12. The method of claim Error! Referencesource not found. wherein a coherent interval of the long-term CSI spansgreater than 1,000 symbols, and the method further comprises: schedulingtransmissions by the at least one UE of a dedicated pilot. 13-14.(canceled)
 15. A central processing system for a distributed cell-freemassive Multiple Input Multiple Output (MIMO) network for broadcastingor multicasting data to User Equipments (UEs), wherein the centralprocessing system comprising: a network interface; and processingcircuitry associated with the network interface, the processingcircuitry configured to cause the central processing system to: for eachAccess Point, AP of two or more APs in the distributed cell-free MIMOnetwork, receive long-term Channel State Information, CSI, for at leastone UE from the AP; compute a precoding vector, w, for the at least oneUE across all of the two or more APs based on the long-term CSI for theat least one UE received from the two or more APs; and communicate theprecoding vector, w, to the two or more APs.
 16. A method performed atan Access Point, AP, in a distributed cell-free massive Multiple InputMultiple Output (MIMO) network for broadcasting or multi-casting data toUser Equipments (UEs) the network comprising two or more APs, and themethod comprising: obtaining long-term Channel State Information, CSI,for at least one UE; communicating the long-term CSI for the at leastone UE to a central processing system; obtaining a precoding vector, w,from the central processing system, the precoding vector, w, being forthe at least one UE across all of two or more APs; precoding data to bebroadcast or multicast to the at least one UE based on the precodingvector, w; and broadcasting or multicasting the precoded data to the atleast one UE.
 17. The method of claim Error! Reference source not found.wherein the at least one UE is two or more UEs.
 18. The method of claimError! Reference source not found. wherein the long-term CSI comprisesestimated path loss.
 119. The method of claim Error! Reference sourcenot found. wherein a coherent interval of the long-term CSI spansgreater than 1,000 symbols, and: obtaining the long-term CSI for the atleast one UE comprises estimating the long-term CSI for the at least oneUE in fixed intervals of time, based on transmissions of a dedicatedpilot by the at least one UE. 20-21. (canceled)
 22. An Access Point, AP,for a distributed cell-free massive Multiple Input Multiple Output,MIMO, network for broadcasting or multicasting data to User Equipments(UEs) wherein the distributed cell-free massive MIMO network comprisestwo or more APs, the AP comprising: a network interface; at least onetransmitter; at least one receiver; and processing circuitry associatedwith the network interface, the at least one transmitter, and the atleast one receiver, wherein the processing circuitry is configured tocause the AP to: obtain long-term Channel State Information, CSI, for atleast one UE; communicate the long-term CSI for the at least one UE to acentral processing system; obtain a precoding vector, w, from thecentral processing system, the precoding vector, w, being for the atleast one UE across all of two or more APs; precode data to be broadcastor multicast to the at least one UE based on the precoding vector, w;and broadcast or multicast the precoded data to the at least one UE.